PROJECT SUMMARY - BIOSTATISTICS The Wilmer Biostatistics Center (WBC) has been successful in providing study design and analytic support to funded Wilmer faculty through its flexible structure that combines the expertise of Wilmer biostatisticians and epidemiologists with consultants at Department of Biostatistics at Johns Hopkins Bloomberg School of Public. The WBC has facilitated interdisciplinary collaboration across schools and across institutions through funding that supports outside consultants. It has also promoted productivity by providing statistical resources to faculty in a timely and cost-efficient manner. As Wilmer researchers become more successful and expand their research portfolios, the WBC is committed to innovating to provide more expansive sustainable services through innovative enhancements. The WBC web presence is expanding to provide more visibility and accessibility, and harnessing web-based assessment tools to refine capture of metrics of success ? information that will help the WBC grow and improve. The WBC is also expanding capacity into new and current data sources, which will allow researchers to harness current research trends in big data sources and sophisticated ophthalmic imaging modalities. The WBC is going beyond providing analytic services to train Wilmer researchers to be savvy consumers of statistical output. The goal of the WBC is to grow and adapt with Wilmer researchers to provide expert reliable analytic services and consulting as well as push frontiers of research in Wilmer through education initiatives, collaboration facilitation, methodological development and capacity building. In the current proposed funding period, the center will 1) continue to provide expert and specialized biostatistics and epidemiology services for ophthalmology research; 2) develop improved tracking of outcomes through web-based technology; 3) refine the WBC business plan to improve transparency and standardize service quality; 4) expand training and educational resources for investigators; and 5) build capacity in new areas of big data.